Optimising startup value chain efficiency using discrete scenario-based robust network data envelopment analysis and weakest link theory

This is the authors' abstract. We don't add key points for this paper.

Although startups drive innovation, economic growth, and new employment opportunities, they are inherently vulnerable to uncertainty. This research proposes an innovative and robust approach to forecasting the efficiency of startups' value chains while accounting for inherent uncertainty. The methodology employs robust network data envelopment analysis (RNDEA) within a three-stage structure. Using Weibull's weakest-link theory, the startup's value chain's overall efficiency is formulated to maximise the minimum (weakest) efficiency across its stages. A scenario-based approach is employed to develop the model's robust counterpart. A case study involving information and communication technology (ICT) startups from an emerging economy demonstrates the applicability of the proposed method by analysing three economic scenarios: (i) recession, (ii) stability, and (iii) growth. The findings reveal that the weakest stage of a startup’s value chain is expected to have at least 27% inefficiency. While the production stage showed consistent performance across different scenarios, the design stage exhibited the weakest efficiency in stability and growth scenarios. This study's novel integration of Weibull weakest-link theory and RNDEA within a scenario-based robust model has practical implications for startup operations, offering a new perspective on how startups can enhance their value chain efficiency.